DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
This office action is in response to the most recent filings filed by applicant on 04/16/26.
Claims 1, 8, and 14-15 are amended
Claims 2-4, 9-11 and 16-18 are cancelled
No claims are added
Claims 1, 5-8, 12-15, and 19-20 are pending
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 5-8, 12-15, and 19-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1, 5-7 is/are directed to a system which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 8, 12-14 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 15, 19-20 is/are directed to a computer storage medium, which is a statutory category. In applicants originally submitted specification describes the above limitation in [0117] as Computer storage media, such as a memory 1622, include volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or the like. In light of this description, the computer storage medium is considered a statutory category, as a device or apparatus claim.
Step 2A Prong 1: Identify the Abstract Idea(s)
The Alice framework, steps 2A-Prong One (part 1 of Mayo Test), here, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP 2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Independent claims 1, 8 and 15, with respect to the Step 2A, Prong One, when “taken as a whole” the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, Independent Method/ System/ Apparatus Claim 8 is directed to an abstract idea, as evidenced by claim limitations “receiving alert configuration data associated with an alert, the configuration data in a plain language format and including a data path relating to a data source associated with the alert; generating an alert rule formatted based on the alert configuration data; accessing a data value stored at the data source using the data path of the alert rule; analyzing the accessed data value associated with the alert and trained in time-series anomaly detection; determining that the alert is triggered based on analyzing the accessed data value; and sending an alert communication of the triggered alert using at least one alert communication channel defined in the received alert configuration data.”
The specification recites in [0001] Businesses monitor the performance of business activities by measuring various performance metrics. For retail industries, the performance metrics may be number of orders, number of returns, delays in refunds, items out of stock, and many more. Some of these metrics are known, while others are hidden and need intelligence to relate business activities to the performance indicators. Usually, analysts study the data and present insights to the business when the performance metrics are known.
In light of this, these claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing business performance metrics for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claims 1 and 15 is/are recite substantially similar limitations to independent claim 8 and is/are rejected under 2A for similar reasons to claim 8 above.
Step 2A Prong 2: Additional Elements That Integrate the Judicial Exception into a Practical Application
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “A system comprising: a processor; and a memory comprising computer program code, the memory and the computer program code configured to cause the processor to: A computerized method comprising: A computer storage medium has computer-executable instructions that, upon execution by a processor, cause the processor to at least: providing an alert configuration interface; via the provided alert configuration interface; via the provided alert configuration interface, in a computer-readable format, using a generative artificial intelligence (AI) model; using a trained machine learning (ML) anomaly detection model; using the trained ML anomaly detection model;” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea with no significantly more elements.
Applicants originally submitted specification describes the computer components above at least in [0116]-[0124]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers").
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 8 and 15 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
Similarly dependent claims 5-7, 12-14 and 19-20 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 12 recite “further comprising, in response to determining that the alert is triggered, performing a de-duplication process before sending the alert communication”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 13 recites “further comprising, in response to determining that the alert is triggered, logging alert data related to the alert for use in providing information about business performance via a business performance interface.” Dependent claims 14 recite “wherein the trained ML anomaly detection model and comprises model training, model validation, and model deployment.” In these claims, “a business performance interface, the trained ML anomaly detection model” are additional element, but it is still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 5-7, 12-14 and 19-20 are also directed to the abstract idea identified above.
The additional elements of a “machine learning model”, “AI model” and “artificial intelligence”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. ***
Step 2B: Determine Whether Any Element, Or Combination, Amount to “Significantly More” Than the Abstract Idea Itself
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A system comprising: a processor; and a memory comprising computer program code, the memory and the computer program code configured to cause the processor to: A computerized method comprising: A computer storage medium has computer-executable instructions that, upon execution by a processor, cause the processor to at least: providing an alert configuration interface; via the provided alert configuration interface; via the provided alert configuration interface, in a computer-readable format, using a generative artificial intelligence (AI) model; using a trained machine learning (ML) anomaly detection model; using the trained ML anomaly detection model;” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in [0116]-[0124]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 1 and 15 is/are recite substantially similar limitations to independent claim 8 and is/are rejected under 2B for similar reasons to claim 8 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 8 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106. (see section III A of Berkheimer memo).
Similarly, dependent claims 5-7, 12-14 and 19-20 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 8 and 15. As a result, Examiner asserts that dependent claims, such as dependent claims 5-7, 12-14 and 19-20 are also directed to the abstract idea identified above.
Further, Examiner notes that the addition limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 5-8, 12-15, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 2018/0114128 A1) Libert et al., and further in view of (US 2023/0267372 A1) Ravi
As per claims 1, 8 and 15: Regarding the claim limitations below, Reference Libert shows: Abstract: clearing a blindspot in the way business leaders, analysts and investors make decisions about capital investments in various businesses, the present inventors devised, among other things, a machine learning based composite classification, search, and analysis systems and methods. One exemplary system automatically classifies businesses based on quantitative and qualitative business data according to a 4-class framework that spans traditional industry boundaries. This classification are based on a combination of spending patterns, financial metrics, and language to identify each firm's business model. The resulting business model is then utilized in conjunction with additional financial and non-financial metrics, securities analysis, leading and lagging indicators, and/or industry comparison to produce a score which can be used to compare business performance within and across classifications to generate superior performance and mitigate risks for business leaders and investment managers (Abstract):
Regarding the claim limitations below, Reference Libert shows:
A system comprising: a processor; and a memory comprising computer program code, the memory and the computer program code configured to cause the processor to (Libert: [0030] Some embodiments of the invention include a system and method that classifies businesses, based on reported financial and non-financial, as well as qualitative data, into a finite set of four or more industry-agnostic or industry-independent business model classes, such as asset builder, service provider, technology creator, and network orchestrator. [0049] Processor module 131, which includes one or more processors, processing circuits, or controllers, is coupled to memory 132. Memory 132 stores code (machine-readable or executable instructions) for an operating system 136, a browser 137, and a graphical user interface (GUI) 138 (defined in whole or part by various modules within server 120). In the exemplary embodiment, operating system 136 and browser 137 not only receive inputs from keyboard 134 and selector 135, but also support rendering of GUI 138 on display 133.):
Regarding the claim limitations below, Reference Libert shows:
A computerized method comprising (Libert: [0030] Some embodiments of the invention include a system and method that classifies businesses, based on reported financial and non-financial, as well as qualitative data, into a finite set of four or more industry-agnostic or industry-independent business model classes, such as asset builder, service provider, technology creator, and network orchestrator.):
Regarding the claim limitations below, Reference Libert shows:
A computer storage medium has computer-executable instructions that, upon execution by a processor, cause the processor to at least (Libert: [0136] Moreover, some embodiments can be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (e.g., including a processor) to perform a method as described and claimed herein. Likewise, computer-readable storage medium can include a non-transitory machine-readable storage device, having stored thereon a computer program (machine executable instructions) that include a plurality of code sections for performing operations, steps or actions as described herein. [0137] Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory) and a Flash memory. Further, it is expected that one of ordinary skill, notwithstanding possibly significant effort and many design choices motivated by, for example, available time, current technology, and economic considerations, when guided by the concepts and principles disclosed herein will be readily capable of generating such software instructions and programs and ICs with minimal experimentation.):
Regarding the claim limitations below, Reference Libert shows:
providing an alert configuration interface (Libert: [0013] Moreover, some embodiments monitor key business model indicators and alert business leaders if indicators suggest a business model evolving in an unintended way. In one embodiment, for example, the research and development (R&D) investment by a selected company is monitored as a proportion of sales. If a decline is detected, say from 17% to 10%, the system alerts the leader to the fact that capital investment patterns are not supporting the technology creator business model. This would allow the business leader to proactively reconsider and readjust allocations to support the desired business model. [0032]: Data stores 110, which take the exemplary form of one or more electronic, magnetic, or optical data-storage devices, are coupled or couplable via a wireless or wireline communications network, such as a local-, wide-, private-, or virtual-private network, to server 120, enabling data interchange via application program interface, JavaScript Object Notation, or electronic data interchange, or any convenient or desirable way of communicating data. [0036]: In addition to one or more application program interfaces (APIs) (not shown) for accessing external data sources 110 or portions thereof associated with or accessible to specific users, user data module 123 includes user data structures, of which data structures 1231 is generally representative. Data structure 1231 includes a user identifier portion 1231A, which is logically associated with one or more data fields or objects 1231B-1231D. [0048] Access device 130 is generally representative of one or more access devices. In the exemplary embodiment, access device 130, like access device 110, takes the form of a personal computer, workstation, personal digital assistant, mobile telephone, kiosk, or any other device capable of providing an effective user interface with a server or database. Specifically, access device 130 includes a processor module 131, a memory 132, a display 133, a keyboard 134, and a graphical pointer or selector 135. (In some embodiments, display 133 includes a touch screen capability. [0049] Processor module 131, which includes one or more processors, processing circuits, or controllers, is coupled to memory 132. Memory 132 stores code (machine-readable or executable instructions) for an operating system 136, a browser 137, and a graphical user interface (GUI) 138 (defined in whole or part by various modules within server 120). In the exemplary embodiment, operating system 136 and browser 137 not only receive inputs from keyboard 134 and selector 135, but also support rendering of GUI 138 on display 133. [0050] Upon rendering, GUI 138, shown on display 133 as GUI 138′, presents data in association with one or more interactive control features (or user-interface elements). In the exemplary embodiment, each of these control features takes the form of a hyperlink or other browser-compatible command input, and provides access to and control of various regions of the graphical user interfaces described herein.);
Regarding the claim limitations below, Reference Libert shows:
receiving alert configuration data associated with an alert via the provided alert configuration interface, the configuration data in a plain language format and including a data path relating to a data source associated with the alert (Libert: [0013] Moreover, some embodiments monitor key business model indicators and alert business leaders if indicators suggest a business model evolving in an unintended way. In one embodiment, for example, the research and development (R&D) investment by a selected company is monitored as a proportion of sales. If a decline is detected, say from 17% to 10%, the system alerts the leader to the fact that capital investment patterns are not supporting the technology creator business model. This would allow the business leader to proactively reconsider and readjust allocations to support the desired business model. [0032]: Data stores 110, which take the exemplary form of one or more electronic, magnetic, or optical data-storage devices, are coupled or couplable via a wireless or wireline communications network, such as a local-, wide-, private-, or virtual-private network, to server 120, enabling data interchange via application program interface, JavaScript Object Notation, or electronic data interchange, or any convenient or desirable way of communicating data. [0036]: In addition to one or more application program interfaces (APIs) (not shown) for accessing external data sources 110 or portions thereof associated with or accessible to specific users, user data module 123 includes user data structures, of which data structures 1231 is generally representative. Data structure 1231 includes a user identifier portion 1231A, which is logically associated with one or more data fields or objects 1231B-1231D. [0048] Access device 130 is generally representative of one or more access devices. In the exemplary embodiment, access device 130, like access device 110, takes the form of a personal computer, workstation, personal digital assistant, mobile telephone, kiosk, or any other device capable of providing an effective user interface with a server or database. Specifically, access device 130 includes a processor module 131, a memory 132, a display 133, a keyboard 134, and a graphical pointer or selector 135. (In some embodiments, display 133 includes a touch screen capability. [0049] Processor module 131, which includes one or more processors, processing circuits, or controllers, is coupled to memory 132. Memory 132 stores code (machine-readable or executable instructions) for an operating system 136, a browser 137, and a graphical user interface (GUI) 138 (defined in whole or part by various modules within server 120). In the exemplary embodiment, operating system 136 and browser 137 not only receive inputs from keyboard 134 and selector 135, but also support rendering of GUI 138 on display 133. [0050] Upon rendering, GUI 138, shown on display 133 as GUI 138′, presents data in association with one or more interactive control features (or user-interface elements). In the exemplary embodiment, each of these control features takes the form of a hyperlink or other browser-compatible command input, and provides access to and control of various regions of the graphical user interfaces described herein. Libert shows converting data received into computer code: [0033] Server 120, which is generally representative of one or more servers for serving data in a variety of desirable form, including for example webpages or other markup language forms with associated applets, remote-invocation objects, or other related software and data structures to service clients of various “thicknesses.” More particularly, server 120 includes a processor module 121, a memory module 122. [0046] Learner module 128 includes data and machine-executable instructions for updating the machine language training data and generating new coefficients and/or other parameters that govern how classifications are determined within the system. For example, some embodiments prompt specific users to manually approve or disapprove of a dominant classification for a company, analyze the quantity and quality of these responses and, if deemed appropriate, change the dominant classification of the company to reflect group sentiment. Users may also be prompted to add the company and its corresponding data to the training data for production of next generation classification profile.
Even though Reference Libert shows the ability to identify data that seems unusual and leading to a user notification or alert to flag such data. Reference Libert does not explicitly show “plain language format”. Ravi also shows “plain language format” ([0033] Server 120, which is generally representative of one or more servers for serving data in a variety of desirable form, including for example webpages or other markup language forms with associated applets, remote-invocation objects, or other related software and data structures to service clients of various “thicknesses.” More particularly, server 120 includes a processor module 121, a memory module 122. [0046] Learner module 128 includes data and machine-executable instructions for updating the machine language training data and generating new coefficients and/or other parameters that govern how classifications are determined within the system. For example, some embodiments prompt specific users to manually approve or disapprove of a dominant classification for a company, analyze the quantity and quality of these responses and, if deemed appropriate, change the dominant classification of the company to reflect group sentiment. Users may also be prompted to add the company and its corresponding data to the training data for production of next generation classification profile.)
Reference Libert and Reference Ravi are analogous prior art to the claimed invention because the references generally relate to field of analyzing data and notifying user when unusual events are noticed. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Ravi, particularly the anomaly detection rule or a trained anomaly detection model [0027], in the disclosure of Reference Libert, particularly in the ability to identify data that seems unusual and leading to a user notification or alert to flag such data ([0047]-[0051]), in order to provide for a machine learning system, which may be also referred to as a “model management system” due to its focus on the model development and training processes. As illustrated, the model management system 100 may be a network-based specialized computer environment configured to efficiently develop and deploy a machine learning model or a set of machine learning models (may be simply referred to as “models”) or AI engines, which can be scaled to different domains and business needs across different platforms, devices, and modalities. As illustrated in FIG. 1, the model management system 100 may include one or more specialized computers or other machines that are configured to develop, train, and deploy machine learning models, and/or apply the deployed machine learning models (e.g., by reference engine) for content recommendation, auto-messaging, document classification, anomaly detection, user authentication, and many other applications, as taught by Reference Ravi (see at least in [0027]), where upon the execution of the method and system of Reference Ravi for generating a personalized machine learning model for the user based on the training of the machine learning model (Ravi: Abstract) so that the process of managing real estate transactions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data and notifying user when unusual events are noticed field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Libert in view of Reference Ravi, the results of the combination were predictable (MPEP 2143 A));
Regarding the claim limitations below, Reference Libert shows:
generating an alert rule formatted in a computer-readable format based on the alert configuration data using a generative artificial intelligence (AI) model (Libert: [0013] Moreover, some embodiments monitor key business model indicators and alert business leaders if indicators suggest a business model evolving in an unintended way. In one embodiment, for example, the research and development (R&D) investment by a selected company is monitored as a proportion of sales. If a decline is detected, say from 17% to 10%, the system alerts the leader to the fact that capital investment patterns are not supporting the technology creator business model. This would allow the business leader to proactively reconsider and readjust allocations to support the desired business model. [0032]: Data stores 110, which take the exemplary form of one or more electronic, magnetic, or optical data-storage devices, are coupled or couplable via a wireless or wireline communications network, such as a local-, wide-, private-, or virtual-private network, to server 120, enabling data interchange via application program interface, JavaScript Object Notation, or electronic data interchange, or any convenient or desirable way of communicating data. [0036]: In addition to one or more application program interfaces (APIs) (not shown) for accessing external data sources 110 or portions thereof associated with or accessible to specific users, user data module 123 includes user data structures, of which data structures 1231 is generally representative. Data structure 1231 includes a user identifier portion 1231A, which is logically associated with one or more data fields or objects 1231B-1231D. [0048] Access device 130 is generally representative of one or more access devices. In the exemplary embodiment, access device 130, like access device 110, takes the form of a personal computer, workstation, personal digital assistant, mobile telephone, kiosk, or any other device capable of providing an effective user interface with a server or database. Specifically, access device 130 includes a processor module 131, a memory 132, a display 133, a keyboard 134, and a graphical pointer or selector 135. (In some embodiments, display 133 includes a touch screen capability. [0049] Processor module 131, which includes one or more processors, processing circuits, or controllers, is coupled to memory 132. Memory 132 stores code (machine-readable or executable instructions) for an operating system 136, a browser 137, and a graphical user interface (GUI) 138 (defined in whole or part by various modules within server 120). In the exemplary embodiment, operating system 136 and browser 137 not only receive inputs from keyboard 134 and selector 135, but also support rendering of GUI 138 on display 133. [0050] Upon rendering, GUI 138, shown on display 133 as GUI 138′, presents data in association with one or more interactive control features (or user-interface elements). In the exemplary embodiment, each of these control features takes the form of a hyperlink or other browser-compatible command input, and provides access to and control of various regions of the graphical user interfaces described herein. Libert shows converting data received into computer code: [0033] Server 120, which is generally representative of one or more servers for serving data in a variety of desirable form, including for example webpages or other markup language forms with associated applets, remote-invocation objects, or other related software and data structures to service clients of various “thicknesses.” More particularly, server 120 includes a processor module 121, a memory module 122. [0046] Learner module 128 includes data and machine-executable instructions for updating the machine language training data and generating new coefficients and/or other parameters that govern how classifications are determined within the system. For example, some embodiments prompt specific users to manually approve or disapprove of a dominant classification for a company, analyze the quantity and quality of these responses and, if deemed appropriate, change the dominant classification of the company to reflect group sentiment. Users may also be prompted to add the company and its corresponding data to the training data for production of next generation classification profile.
Even though Reference Libert shows the ability to identify data that seems unusual and leading to a user notification or alert to flag such data, which is reasonably understood to read on “a generative artificial intelligence (AI) model”, Libert does not use the exact words to describe the process that leads to the alert. As such, Reference Ravi shows the above limitations in [0027]: the model management system 100 may include one or more specialized computers or other machines that are configured to develop, train, and deploy machine learning models, and/or apply the deployed machine learning models (e.g., by reference engine) for content recommendation, auto-messaging, document classification, anomaly detection, user authentication, and many other applications. Ravi also shows “plain language format to a computer code format” ([0033] Server 120, which is generally representative of one or more servers for serving data in a variety of desirable form, including for example webpages or other markup language forms with associated applets, remote-invocation objects, or other related software and data structures to service clients of various “thicknesses.” More particularly, server 120 includes a processor module 121, a memory module 122. [0046] Learner module 128 includes data and machine-executable instructions for updating the machine language training data and generating new coefficients and/or other parameters that govern how classifications are determined within the system. For example, some embodiments prompt specific users to manually approve or disapprove of a dominant classification for a company, analyze the quantity and quality of these responses and, if deemed appropriate, change the dominant classification of the company to reflect group sentiment. Users may also be prompted to add the company and its corresponding data to the training data for production of next generation classification profile.)
Reference Libert and Reference Ravi are analogous prior art to the claimed invention because the references generally relate to field of analyzing data and notifying user when unusual events are noticed. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Ravi, particularly the anomaly detection rule or a trained anomaly detection model [0027], in the disclosure of Reference Libert, particularly in the ability to identify data that seems unusual and leading to a user notification or alert to flag such data ([0047]-[0051]), in order to provide for a machine learning system, which may be also referred to as a “model management system” due to its focus on the model development and training processes. As illustrated, the model management system 100 may be a network-based specialized computer environment configured to efficiently develop and deploy a machine learning model or a set of machine learning models (may be simply referred to as “models”) or AI engines, which can be scaled to different domains and business needs across different platforms, devices, and modalities. As illustrated in FIG. 1, the model management system 100 may include one or more specialized computers or other machines that are configured to develop, train, and deploy machine learning models, and/or apply the deployed machine learning models (e.g., by reference engine) for content recommendation, auto-messaging, document classification, anomaly detection, user authentication, and many other applications, as taught by Reference Ravi (see at least in [0027]), where upon the execution of the method and system of Reference Ravi for generating a personalized machine learning model for the user based on the training of the machine learning model (Ravi: Abstract) so that the process of managing real estate transactions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data and notifying user when unusual events are noticed field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Libert in view of Reference Ravi, the results of the combination were predictable (MPEP 2143 A));
Regarding the claim limitations below, Reference Libert shows:
accessing a data value stored at the data source using the data path of the alert rule (Libert: [0112] Additionally, some embodiments may utilize database 125 to retrieve the classifications and scores for two or more businesses for purposes of direct comparison and contrast, as well as identifying trends and correlations and opportunities for improvement against peers. For example, an experienced management professional or expert AI driven software module may be able to identify allocation patterns, such as acquisitions or capital investment, that are associated with higher scoring and ranking in this method. By tracking these patterns within the context of the overarching business model, leaders can not only better evaluate the strength and trajectory of current and potential competitors, but also better identify capital allocation decisions which will increase the score and rank of their own organizations, leading to competitive advantage. Management professionals can also gain new insight into their components positioning by tracking changes in business model classification of current or potential competitors, allowing them to better identify new market competition or new market niches which may otherwise have gone unnoticed based on traditional industry-siloed analysis.);
Regarding the claim limitations below, Reference Libert shows:
analyzing the accessed data value using a trained machine learning (ML) anomaly detection model associated with the alert and trained in time-series anomaly detection
Libert shows” analyzing the accessed data value using … associated with the alert and trained in time-series anomaly detection” : [0112] Additionally, some embodiments may utilize database 125 to retrieve the classifications and scores for two or more businesses for purposes of direct comparison and contrast, as well as identifying trends and correlations and opportunities for improvement against peers. For example, an experienced management professional or expert AI driven software module may be able to identify allocation patterns, such as acquisitions or capital investment, that are associated with higher scoring and ranking in this method. By tracking these patterns within the context of the overarching business model, leaders can not only better evaluate the strength and trajectory of current and potential competitors, but also better identify capital allocation decisions which will increase the score and rank of their own organizations, leading to competitive advantage. Management professionals can also gain new insight into their components positioning by tracking changes in business model classification of current or potential competitors, allowing them to better identify new market competition or new market niches which may otherwise have gone unnoticed based on traditional industry-siloed analysis. [0047] Some embodiments include additional modulus for retrieving forward looking statements from annual reports and other corporate filings, performing sentiment analysis and text mining of these statements and determining based on logistical regression analysis or similarity metrics whether the statements and/or business decision data indicate that the business actions are in alignment with each other, providing alerts and other outputs to business leaders, investors, outlets, etc. [0051]: Business model monitor region 1383 enables users to identify one or more business for business model monitoring. In some embodiments, it allows the user to enter a business and request to be notified if one or more financial or non-financial parameters that played a principle role the business's current business model classification or current business model composite score deviates by a certain percentage from a threshold amount or outside of a predetermined range. [0117] In exemplary embodiment construction of a system and method according to the present invention utilizing Classification Data, E2, as the data source, the threat analysis product will track the primary business model measure, PPE/Total Assets percentage, E4, Service Provider terms percentage, E16, R&D/Revenue percentage, E28 AND Network Orchestrator terms percentage, E40. These measures are broken down into ‘flag’ and ‘alert’ thresholds, wherein a flag is a notation of a trend and an alert is triggered when a measure has reached the requirement for potential secondary designation.
Libert shows: “associated with the alert and trained in time-series anomaly detection” [0112] Additionally, some embodiments may utilize database 125 to retrieve the classifications and scores for two or more businesses for purposes of direct comparison and contrast, as well as identifying trends and correlations and opportunities for improvement against peers. For example, an experienced management professional or expert AI driven software module may be able to identify allocation patterns, such as acquisitions or capital investment, that are associated with higher scoring and ranking in this method. By tracking these patterns within the context of the overarching business model, leaders can not only better evaluate the strength and trajectory of current and potential competitors, but also better identify capital allocation decisions which will increase the score and rank of their own organizations, leading to competitive advantage. Management professionals can also gain new insight into their components positioning by tracking changes in business model classification of current or potential competitors, allowing them to better identify new market competition or new market niches which may otherwise have gone unnoticed based on traditional industry-siloed analysis. [0047] Some embodiments include additional modulus for retrieving forward looking statements from annual reports and other corporate filings, performing sentiment analysis and text mining of these statements and determining based on logistical regression analysis or similarity metrics whether the statements and/or business decision data indicate that the business actions are in alignment with each other, providing alerts and other outputs to business leaders, investors, outlets, etc. [0051]: Business model monitor region 1383 enables users to identify one or more business for business model monitoring. In some embodiments, it allows the user to enter a business and request to be notified if one or more financial or non-financial parameters that played a principle role the business's current business model classification or current business model composite score deviates by a certain percentage from a threshold amount or outside of a predetermined range. [0117] In exemplary embodiment construction of a system and method according to the present invention utilizing Classification Data, E2, as the data source, the threat analysis product will track the primary business model measure, PPE/Total Assets percentage, E4, Service Provider terms percentage, E16, R&D/Revenue percentage, E28 AND Network Orchestrator terms percentage, E40. These measures are broken down into ‘flag’ and ‘alert’ thresholds, wherein a flag is a notation of a trend and an alert is triggered when a measure has reached the requirement for potential secondary designation.
Even though Reference Libert shows the ability to identify data that seems unusual and leading to a user notification or alert to flag such data, which is reasonably understood to read on “using a trained machine learning (ML) anomaly detection model”, Libert does not use the exact words to describe the process that leads to the alert. As such, Reference Ravi shows the above limitations in [0027]: the model management system 100 may include one or more specialized computers or other machines that are configured to develop, train, and deploy machine learning models, and/or apply the deployed machine learning models (e.g., by reference engine) for content recommendation, auto-messaging, document classification, anomaly detection, user authentication, and many other applications.
Reference Libert and Reference Ravi are analogous prior art to the claimed invention because the references generally relate to field of analyzing data and notifying user when unusual events are noticed. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Ravi, particularly the anomaly detection rule or a trained anomaly detection model [0027], in the disclosure of Reference Libert, particularly in the ability to identify data that seems unusual and leading to a user notification or alert to flag such data ([0047]-[0051]), in order to provide for a machine learning system, which may be also referred to as a “model management system” due to its focus on the model development and training processes. As illustrated, the model management system 100 may be a network-based specialized computer environment configured to efficiently develop and deploy a machine learning model or a set of machine learning models (may be simply referred to as “models”) or AI engines, which can be scaled to different domains and business needs across different platforms, devices, and modalities. As illustrated in FIG. 1, the model management system 100 may include one or more specialized computers or other machines that are configured to develop, train, and deploy machine learning models, and/or apply the deployed machine learning models (e.g., by reference engine) for content recommendation, auto-messaging, document classification, anomaly detection, user authentication, and many other applications, as taught by Reference Ravi (see at least in [0027]), where upon the execution of the method and system of Reference Ravi for generating a personalized machine learning model for the user based on the training of the machine learning model (Ravi: Abstract) so that the process of managing real estate transactions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data and notifying user when unusual events are noticed field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Libert in view of Reference Ravi, the results of the combination were predictable (MPEP 2143 A).
Regarding the claim limitations below, Reference Libert and Reference Ravi shows:
determining that the alert is triggered based on analyzing the accessed data value using the trained ML anomaly detection model (Libert shows: [0112] Additionally, some embodiments may utilize database 125 to retrieve the classifications and scores for two or more businesses for purposes of direct comparison and contrast, as well as identifying trends and correlations and opportunities for improvement against peers. For example, an experienced management professional or expert AI driven software module may be able to identify allocation patterns, such as acquisitions or capital investment, that are associated with higher scoring and ranking in this method. By tracking these patterns within the context of the overarching business model, leaders can not only better evaluate the strength and trajectory of current and potential competitors, but also better identify capital allocation decisions which will increase the score and rank of their own organizations, leading to competitive advantage. Management professionals can also gain new insight into their components positioning by tracking changes in business model classification of current or potential competitors, allowing them to better identify new market competition or new market niches which may otherwise have gone unnoticed based on traditional industry-siloed analysis. [0047] Some embodiments include additional modulus for retrieving forward looking statements from annual reports and other corporate filings, performing sentiment analysis and text mining of these statements and determining based on logistical regression analysis or similarity metrics whether the statements and/or business decision data indicate that the business actions are in alignment with each other, providing alerts and other outputs to business leaders, investors, outlets, etc. [0051]: Business model monitor region 1383 enables users to identify one or more business for business model monitoring. In some embodiments, it allows the user to enter a business and request to be notified if one or more financial or non-financial parameters that played a principle role the business's current business model classification or current business model composite score deviates by a certain percentage from a threshold amount or outside of a predetermined range. [0117] In exemplary embodiment construction of a system and method according to the present invention utilizing Classification Data, E2, as the data source, the threat analysis product will track the primary business model measure, PPE/Total Assets percentage, E4, Service Provider terms percentage, E16, R&D/Revenue percentage, E28 AND Network Orchestrator terms percentage, E40. These measures are broken down into ‘flag’ and ‘alert’ thresholds, wherein a flag is a notation of a trend and an alert is triggered when a measure has reached the requirement for potential secondary designation. Libert shows [0112] Additionally, some embodiments may utilize database 125 to retrieve the classifications and scores for two or more businesses for purposes of direct comparison and contrast, as well as identifying trends and correlations and opportunities for improvement against peers. For example, an experienced management professional or expert AI driven software module may be able to identify allocation patterns, such as acquisitions or capital investment, that are associated with higher scoring and ranking in this method. By tracking these patterns within the context of the overarching business model, leaders can not only better evaluate the strength and trajectory of current and potential competitors, but also better identify capital allocation decisions which will increase the score and rank of their own organizations, leading to competitive advantage. Management professionals can also gain new insight into their components positioning by tracking changes in business model classification of current or potential competitors, allowing them to better identify new market competition or new market niches which may otherwise have gone unnoticed based on traditional industry-siloed analysis. [0047] Some embodiments include additional modulus for retrieving forward looking statements from annual reports and other corporate filings, performing sentiment analysis and text mining of these statements and determining based on logistical regression analysis or similarity metrics whether the statements and/or business decision data indicate that the business actions are in alignment with each other, providing alerts and other outputs to business leaders, investors, outlets, etc. [0051]: Business model monitor region 1383 enables users to identify one or more business for business model monitoring. In some embodiments, it allows the user to enter a business and request to be notified if one or more financial or non-financial parameters that played a principle role the business's current business model classification or current business model composite score deviates by a certain percentage from a threshold amount or outside of a predetermined range. [0117] In exemplary embodiment construction of a system and method according to the present invention utilizing Classification Data, E2, as the data source, the threat analysis product will track the primary business model measure, PPE/Total Assets percentage, E4, Service Provider terms percentage, E16, R&D/Revenue percentage, E28 AND Network Orchestrator terms percentage, E40. These measures are broken down into ‘flag’ and ‘alert’ thresholds, wherein a flag is a notation of a trend and an alert is triggered when a measure has reached the requirement for potential secondary designation.
Libert shows: “associated with the alert and trained in time-series anomaly detection” [0112] Additionally, some embodiments may utilize database 125 to retrieve the classifications and scores for two or more businesses for purposes of direct comparison and contrast, as well as identifying trends and correlations and opportunities for improvement against peers. For example, an experienced management professional or expert AI driven software module may be able to identify allocation patterns, such as acquisitions or capital investment, that are associated with higher scoring and ranking in this method. By tracking these patterns within the context of the overarching business model, leaders can not only better evaluate the strength and trajectory of current and potential competitors, but also better identify capital allocation decisions which will increase the score and rank of their own organizations, leading to competitive advantage. Management professionals can also gain new insight into their components positioning by tracking changes in business model classification of current or potential competitors, allowing them to better identify new market competition or new market niches which may otherwise have gone unnoticed based on traditional industry-siloed analysis. [0047] Some embodiments include additional modulus for retrieving forward looking statements from annual reports and other corporate filings, performing sentiment analysis and text mining of these statements and determining based on logistical regression analysis or similarity metrics whether the statements and/or business decision data indicate that the business actions are in alignment with each other, providing alerts and other outputs to business leaders, investors, outlets, etc. [0051]: Business model monitor region 1383 enables users to identify one or more business for business model monitoring. In some embodiments, it allows the user to enter a business and request to be notified if one or more financial or non-financial parameters that played a principle role the business's current business model classification or current business model composite score deviates by a certain percentage from a threshold amount or outside of a predetermined range. [0117] In exemplary embodiment construction of a system and method according to the present invention utilizing Classification Data, E2, as the data source, the threat analysis product will track the primary business model measure, PPE/Total Assets percentage, E4, Service Provider terms percentage, E16, R&D/Revenue percentage, E28 AND Network Orchestrator terms percentage, E40. These measures are broken down into ‘flag’ and ‘alert’ thresholds, wherein a flag is a notation of a trend and an alert is triggered when a measure has reached the requirement for potential secondary designation.
Even though Reference Libert shows the ability to identify data that seems unusual and leading to a user notification or alert to flag such data, which is reasonably understood to read on “a generative artificial intelligence (AI) model”, Libert does not use the exact words to describe the process that leads to the alert. As such, Reference Ravi shows the above limitations in [0027]: the model management system 100 may include one or more specialized computers or other machines that are configured to develop, train, and deploy machine learning models, and/or apply the deployed machine learning models (e.g., by reference engine) for content recommendation, auto-messaging, document classification, anomaly detection, user authentication, and many other applications.
Reference Libert and Reference Ravi are analogous prior art to the claimed invention because the references generally relate to field of analyzing data and notifying user when unusual events are noticed. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Ravi, particularly the anomaly detection rule or a trained anomaly detection model [0027], in the disclosure of Reference Libert, particularly in the ability to identify data that seems unusual and leading to a user notification or alert to flag such data ([0047]-[0051]), in order to provide for a machine learning system, which may be also referred to as a “model management system” due to its focus on the model development and training processes. As illustrated, the model management system 100 may be a network-based specialized computer environment configured to efficiently develop and deploy a machine learning model or a set of machine learning models (may be simply referred to as “models”) or AI engines, which can be scaled to different domains and business needs across different platforms, devices, and modalities. As illustrated in FIG. 1, the model management system 100 may include one or more specialized computers or other machines that are configured to develop, train, and deploy machine learning models, and/or apply the deployed machine learning models (e.g., by reference engine) for content recommendation, auto-messaging, document classification, anomaly detection, user authentication, and many other applications, as taught by Reference Ravi (see at least in [0027]), where upon the execution of the method and system of Reference Ravi for generating a personalized machine learning model for the user based on the training of the machine learning model (Ravi: Abstract) so that the process of managing real estate transactions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data and notifying user when unusual events are noticed field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Libert in view of Reference Ravi, the results of the combination were predictable (MPEP 2143 A)); and
Regarding the claim limitations below, Reference Libert and Reference Ravi shows:
sending an alert communication of the triggered alert using at least one alert communication channel defined in the received alert configuration data (Libert shows: [0112] Additionally, some embodiments may utilize database 125 to retrieve the classifications and scores for two or more businesses for purposes of direct comparison and contrast, as well as identifying trends and correlations and opportunities for improvement against peers. For example, an experienced management professional or expert AI driven software module may be able to identify allocation patterns, such as acquisitions or capital investment, that are associated with higher scoring and ranking in this method. By tracking these patterns within the context of the overarching business model, leaders can not only better evaluate the strength and trajectory of current and potential competitors, but also better identify capital allocation decisions which will increase the score and rank of their own organizations, leading to competitive advantage. Management professionals can also gain new insight into their components positioning by tracking changes in business model classification of current or potential competitors, allowing them to better identify new market competition or new market niches which may otherwise have gone unnoticed based on traditional industry-siloed analysis. [0047] Some embodiments include additional modulus for retrieving forward looking statements from annual reports and other corporate filings, performing sentiment analysis and text mining of these statements and determining based on logistical regression analysis or similarity metrics whether the statements and/or business decision data indicate that the business actions are in alignment with each other, providing alerts and other outputs to business leaders, investors, outlets, etc. [0051]: Business model monitor region 1383 enables users to identify one or more business for business model monitoring. In some embodiments, it allows the user to enter a business and request to be notified if one or more financial or non-financial parameters that played a principle role the business's current business model classification or current business model composite score deviates by a certain percentage from a threshold amount or outside of a predetermined range. [0117] In exemplary embodiment construction of a system and method according to the present invention utilizing Classification Data, E2, as the data source, the threat analysis product will track the primary business model measure, PPE/Total Assets percentage, E4, Service Provider terms percentage, E16, R&D/Revenue percentage, E28 AND Network Orchestrator terms percentage, E40. These measures are broken down into ‘flag’ and ‘alert’ thresholds, wherein a flag is a notation of a trend and an alert is triggered when a measure has reached the requirement for potential secondary designation.).
As per claims 5, 12 and 19: Regarding the claim limitations below, Reference Libert and Reference Ravi shows:
further comprising, in response to determining that the alert is triggered, performing a de-duplication process before sending the alert communication (Libert: [0051] More particularly, GUI 138 includes, among other things, a business model search region 1381, a classification request region 1382, and a business model monitor region 1383. Business model search region 1381 allows users to define and submit business model queries to server 120, specifically business database 125 for businesses, based on business class and/or one or more other criteria such as industry sector, subsector, market capitalization, etc. Classification request region 1382 allows users to enter or otherwise identify one or more business entities and submit a request that the entity be classified according to one or more selected business model classification schemes. In some embodiments, the available business model classification schemes include the four-class business model scheme described herein, in addition to one or more other business model or industry classifiers defined, for example by the requesting user or another user. Business model monitor region 1383 enables users to identify one or more business for business model monitoring. In some embodiments, it allows the user to enter a business and request to be notified if one or more financial or non-financial parameters that played a principle role the business's current business model classification or current business model composite score deviates by a certain percentage from a threshold amount or outside of a predetermined range. In some embodiments, the monitor includes electronic trading capabilities enabling automatic execution of stock trades in response to detected deviances. The system, in some embodiments, allows monitoring for business models that also transition into desired business models. For example, an asset builder company may be monitored to determine when it makes business decisions that resemble a technology creator or networker orchestrator business model. The determination may trigger an alert or a stock purchase. [0117] In exemplary embodiment construction of a system and method according to the present invention utilizing Classification Data, E2, as the data source, the threat analysis product will track the primary business model measure, PPE/Total Assets percentage, E4, Service Provider terms percentage, E16, R&D/Revenue percentage, E28 AND Network Orchestrator terms percentage, E40. These measures are broken down into ‘flag’ and ‘alert’ thresholds, wherein a flag is a notation of a trend and an alert is triggered when a measure has reached the requirement for potential secondary designation).
As per claims 6, 13 and 20: Regarding the claim limitations below, Reference Libert and Reference Ravi shows:
further comprising, in response to determining that the alert is triggered, logging alert data related to the alert for use in providing information about business performance via a business performance interface (Libert: [0051] More particularly, GUI 138 includes, among other things, a business model search region 1381, a classification request region 1382, and a business model monitor region 1383. Business model search region 1381 allows users to define and submit business model queries to server 120, specifically business database 125 for businesses, based on business class and/or one or more other criteria such as industry sector, subsector, market capitalization, etc. Classification request region 1382 allows users to enter or otherwise identify one or more business entities and submit a request that the entity be classified according to one or more selected business model classification schemes. In some embodiments, the available business model classification schemes include the four-class business model scheme described herein, in addition to one or more other business model or industry classifiers defined, for example by the requesting user or another user. Business model monitor region 1383 enables users to identify one or more business for business model monitoring. In some embodiments, it allows the user to enter a business and request to be notified if one or more financial or non-financial parameters that played a principle role the business's current business model classification or current business model composite score deviates by a certain percentage from a threshold amount or outside of a predetermined range. In some embodiments, the monitor includes electronic trading capabilities enabling automatic execution of stock trades in response to detected deviances. The system, in some embodiments, allows monitoring for business models that also transition into desired business models. For example, an asset builder company may be monitored to determine when it makes business decisions that resemble a technology creator or networker orchestrator business model. The determination may trigger an alert or a stock purchase. [0117] In exemplary embodiment construction of a system and method according to the present invention utilizing Classification Data, E2, as the data source, the threat analysis product will track the primary business model measure, PPE/Total Assets percentage, E4, Service Provider terms percentage, E16, R&D/Revenue percentage, E28 AND Network Orchestrator terms percentage, E40. These measures are broken down into ‘flag’ and ‘alert’ thresholds, wherein a flag is a notation of a trend and an alert is triggered when a measure has reached the requirement for potential secondary designation).
As per claims 7 and 14: Regarding the claim limitations below, Reference Libert and Reference Ravi shows:
wherein the analyzing of the accessed data value is performed by the trained anomaly detection model and comprises model training, model validation, and model deployment.
Libert shows [0112] Additionally, some embodiments may utilize database 125 to retrieve the classifications and scores for two or more businesses for purposes of direct comparison and contrast, as well as identifying trends and correlations and opportunities for improvement against peers. For example, an experienced management professional or expert AI driven software module may be able to identify allocation patterns, such as acquisitions or capital investment, that are associated with higher scoring and ranking in this method. By tracking these patterns within the context of the overarching business model, leaders can not only better evaluate the strength and trajectory of current and potential competitors, but also better identify capital allocation decisions which will increase the score and rank of their own organizations, leading to competitive advantage. Management professionals can also gain new insight into their components positioning by tracking changes in business model classification of current or potential competitors, allowing them to better identify new market competition or new market niches which may otherwise have gone unnoticed based on traditional industry-siloed analysis. [0047] Some embodiments include additional modulus for retrieving forward looking statements from annual reports and other corporate filings, performing sentiment analysis and text mining of these statements and determining based on logistical regression analysis or similarity metrics whether the statements and/or business decision data indicate that the business actions are in alignment with each other, providing alerts and other outputs to business leaders, investors, outlets, etc. [0051]: Business model monitor region 1383 enables users to identify one or more business for business model monitoring. In some embodiments, it allows the user to enter a business and request to be notified if one or more financial or non-financial parameters that played a principle role the business's current business model classification or current business model composite score deviates by a certain percentage from a threshold amount or outside of a predetermined range. [0117] In exemplary embodiment construction of a system and method according to the present invention utilizing Classification Data, E2, as the data source, the threat analysis product will track the primary business model measure, PPE/Total Assets percentage, E4, Service Provider terms percentage, E16, R&D/Revenue percentage, E28 AND Network Orchestrator terms percentage, E40. These measures are broken down into ‘flag’ and ‘alert’ thresholds, wherein a flag is a notation of a trend and an alert is triggered when a measure has reached the requirement for potential secondary designation.
Even though Reference Libert shows the ability to identify data that seems unusual and leading to a user notification or alert to flag such data, which is reasonably understood to read on “a generative artificial intelligence (AI) model”, Libert does not use the exact words to describe the process that leads to the alert. As such, Reference Ravi shows the above limitations in [0027]: the model management system 100 may include one or more specialized computers or other machines that are configured to develop, train, and deploy machine learning models, and/or apply the deployed machine learning models (e.g., by reference engine) for content recommendation, auto-messaging, document classification, anomaly detection, user authentication, and many other applications.
Reference Libert and Reference Ravi are analogous prior art to the claimed invention because the references generally relate to field of analyzing data and notifying user when unusual events are noticed. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Ravi, particularly the anomaly detection rule or a trained anomaly detection model [0027], in the disclosure of Reference Libert, particularly in the ability to identify data that seems unusual and leading to a user notification or alert to flag such data ([0047]-[0051]), in order to provide for a machine learning system, which may be also referred to as a “model management system” due to its focus on the model development and training processes. As illustrated, the model management system 100 may be a network-based specialized computer environment configured to efficiently develop and deploy a machine learning model or a set of machine learning models (may be simply referred to as “models”) or AI engines, which can be scaled to different domains and business needs across different platforms, devices, and modalities. As illustrated in FIG. 1, the model management system 100 may include one or more specialized computers or other machines that are configured to develop, train, and deploy machine learning models, and/or apply the deployed machine learning models (e.g., by reference engine) for content recommendation, auto-messaging, document classification, anomaly detection, user authentication, and many other applications, as taught by Reference Ravi (see at least in [0027]), where upon the execution of the method and system of Reference Ravi for generating a personalized machine learning model for the user based on the training of the machine learning model (Ravi: Abstract) so that the process of managing real estate transactions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data and notifying user when unusual events are noticed field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Libert in view of Reference Ravi, the results of the combination were predictable (MPEP 2143 A).
Response to Arguments
Applicants’ arguments are moot in view of the new grounds of rejection necessitated by the amendments made to previously presented claims.
Applicant’s Argument #1
Applicants’ argue on page(s) 6-7 of applicants remarks that “Accordingly, Applicant contends that the features above do not fall into the methods of organizing human activity grouping of abstract ideas. Indeed, the claimed features related to using an AI model for alert rule generation and a ML anomaly detection model for determining alert triggers do not fall into the interactions associated with methods of organizing human activity.” (see applicants remarks for more details).
Response to Argument #1
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
While the amended claims are related to improvement to the business process, they are not an improvement to the field of machine learning or neural network methods.
Putting data through the machine learning model allows you to use better data, but this is not improving the field of the machine learning.
The specification recites in [0001] Businesses monitor the performance of business activities by measuring various performance metrics. For retail industries, the performance metrics may be number of orders, number of returns, delays in refunds, items out of stock, and many more. Some of these metrics are known, while others are hidden and need intelligence to relate business activities to the performance indicators. Usually, analysts study the data and present insights to the business when the performance metrics are known.
In light of this, these claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing business performance metrics for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Applicant’s Argument #2
Applicants argue on page(s) 7-8 of applicants remarks that “As amended, claim 1 successfully integrates the claim into the particular technological environment of anomaly detection and alerting systems. Claim 1 recites generating alert rules in a computer-readable format using an AI model based on plain language input; accessing data values from a data source based on instructions from the generated alert rule; analyzing the accessed data values using an ML anomaly detection model to determine if an alert is trigged based on the data value; and sending a communication of the triggered alert based on communication paths defined in the receive configuration data. Accordingly, Applicant submits that claim 1 recites features that firmly link the claimed invention to the particular technological environment of anomaly detection and alerting systems and is thus patent-eligible pursuant to step 2A prong 2…. Thus, akin to the multiple independent tactic-specific models of Ex Parte Carmody, claim 1 also recites independently trainable and modifiable models in reciting the AI model and the trained ML anomaly detection model. As those with skill in the art will recognize, each of the AI model and the trained ML anomaly detection model can be modified separately without affecting the other model, just as with the models considered in Ex Parte Carmody.” (see applicants remarks for more details).
Response to Argument #2
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The additional elements discussed in the 101 rejection Step 2A Prong 2 are recited at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea with no significantly more elements.
Applicants originally submitted specification describes the computer components above at least in [0116]-[0124]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers").
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea.
Putting data through an AI model or a machine learning model, detecting an anomaly in the data which sets off a trigger that sends a communication is solving a business problem of managing business performance metrics for one or more human entities. This is not improving the technology or the technological environment. This is very different from Ex Parte Carmody case argued by applicants above. The claims in Ex Parte Carmody integrated the abstract ideas into a practical application by reciting improvements in AI model training and modular architecture, rather than merely applying AI to achieve a business outcome. The claims in Ex Parte Carmody improved the AI system itself, specifically through modular, plug-and-play tactic-specific models that could be updated independently. This technical improvement satisfied the practical application requirement.
Applicant’s Argument #3
Applicants argue on page(s) 8-10 of applicants remarks that “Under step 2B, a claim can be found to amount to significantly more than the judicial exception when the claim recites elements that amount to an inventive concept. MPEP 2106.05(I). Claim elements are required to be considered both individually and in combination when determining if they amount to significantly more than the judicial exception. Id. Here, claim 1 recites features such as generating alert rules in a computer-readable format using an AI model based on plain language input; accessing data values from a data source based on instructions from the generated alert rule; analyzing the accessed data values using an ML anomaly detection model to determine if an alert is trigged based on the data value; and sending communication of the triggered alert to an alert target based on communication paths defined in the received configuration data. Applicant contends that these features furnish an inventive concept that amount to significantly more than the alleged judicial exception of methods of organizing human activities. Indeed, claim 1 now specifically recites multiple machine learning systems working in concert to generate and transmit targeted alerts to appropriate destination relating to detected anomalies.” (see applicants remarks for more details).
Response to Argument #3
Applicants' arguments have been fully considered; however, the examiner respectfully disagrees.
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) discussed above in the 101 rejection under Step 2B are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in [0116]-[0124]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
C. Puri and C. Dukatz, "Analyzing and Predicting Security Event Anomalies: Lessons Learned from a Large Enterprise Big Data Streaming Analytics Deployment," 2015 26th International Workshop on Database and Expert Systems Applications (DEXA), Valencia, Spain, 2015, pp. 152-158, doi: 10.1109/DEXA.2015.46.
This reference discloses a novel and unique live operational and situational awareness implementation bringing big data architectures, graph analytics, streaming analytics, and interactive visualizations to a security use case with data from a large Global 500 company. We present the data acceleration patterns utilized, the employed analytics framework and its complexities, and finally demonstrate the creation of rich interactive visualizations that bring the story of the data acceleration pipeline and analytics to life. We deploy a novel solution to learn typical network agent behaviors and extract the degree to which a network event is anomalous for automatic anomaly rule learning to provide additional context to security alerts. We implement and evaluate the analytics over a data acceleration framework that performs the analysis and model creation at scale in a distributed parallel manner. Additionally, we talk about the acceleration architecture considerations and demonstrate how we complete the analytics story with rich interactive visualizations designed for the security and business analyst alike. This paper concludes with evaluations and lessons learned.
Foreign Reference:
(CN 115495300 A) Chen et al. An automatic interface management method, suitable for interface of enterprise resource planning device, comprising the following steps: the automatic parameter configuration is executed by the processor; displaying the visualization flow chart by the picture of the display, so as to guide the user to perform parameter configuration operation; and automatically replying the interface by the processor when the interface is abnormal, and sending the abnormal message. The implementation can be automatically recovered when the integrated interface is abnormal, so as to ensure the stability of the integrated interface and the service program.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.N.P/Examiner, Art Unit 3624
/HAMZEH OBAID/Primary Examiner, Art Unit 3624
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